Spare parts stocking analysis using genetic programming

Abstract

Optimal solutions to the Level of Repair Analysis (LORA) and the Spare Parts Stocking (SPS) problems are essential in achieving a desired system/equipment operational availability. Although these two problems are interdependent, they are seldom solved simultaneously due to the complicating nature of the relationships between spare levels and system availability (or expected backorder) thus leading to sub-optimal solutions for both problems. This paper uses genetic programming-based symbolic regression methodology to evolve simpler mathematical expressions for the expected backorder equation. In addition to making the SPS problem more tractable, the simpler mathematical expressions make it possible for a combined SPS and LORA model to be formulated and solved using standard optimization techniques. Three sets of spare parts stocking problems are presented to study the feasibility of the proposed approach. Further, a case study for the joint problem is solved which shows that the proposed methodology can tackle the integrated problem.